We assume the following scenario in federated learning. Suppose there are \(M\)  nodes (devices) in the FL network, holding private local data \(\mathcal {D}_i = \lbrace x_{i,j},y_{i,j}\rbrace \)  where \(i\)  is the FL iteration and \(j\)  is the user index. Each node consists of public data \(\mathcal {D}_0=\lbrace x_0,y_0\rbrace \) . The public data is assumed to be present across the global and all local users as discussed in [1]} to handle the various statistical (model) heterogeneities which is a common phenomena in FL. The overall label-set of public dataset is \(Y = \lbrace y_0\rbrace \) , which represent its unique labels. We re-purpose this public dataset as test set and do not expose it to local models during FL training iterations, but expose only during testing for consistency. Our work's main contribution is to propose a framework to identify new labels across different users without transferring private data in FL setting. We also assume each user can stream data with new labels at any iteration which does not belong to public label-set \(Y\) , i.e. \(\mathbf {y}_{i,j} \notin Y\) . In other words, the global user has no idea of these new labels.
